Modelling beneficiaries’ choice in disaster relief logistics

This paper introduces the element of beneficiaries’ choice into a location-routing problem for disaster relief logistics suited for decision support systems. Decision makers in humanitarian logistics face the challenge where to establish distribution centers (DCs) for relief goods. For this purpose,...

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Veröffentlicht in:Annals of operations research Jg. 256; H. 1; S. 41 - 61
Hauptverfasser: Burkart, Christian, Nolz, Pamela C., Gutjahr, Walter J.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.09.2017
Springer Nature B.V
Schlagworte:
Business and Management
Combinatorics
Decision support systems
Disaster relief
Distribution centers
Distribution management
Evolutionary algorithms
Logistics
Mathematical programming
Multiple objective analysis
Operations research
Operations Research/Decision Theory
Support systems
Theory of Computation
Competitive location model
Disaster relief
Evolutionary algorithm
Multi-objective combinatorial optimization
ISSN:0254-5330, 1572-9338
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Zusammenfassung:This paper introduces the element of beneficiaries’ choice into a location-routing problem for disaster relief logistics suited for decision support systems. Decision makers in humanitarian logistics face the challenge where to establish distribution centers (DCs) for relief goods. For this purpose, two objectives are considered: the impact of the relief operations on the beneficiaries and the efficient use of monetary resources. The proposed multi-objective location-routing model minimizes unserved demand as well as cost for opening DCs and for routing relief goods. It anticipates the choice of beneficiaries to which DC to go (if at all), based on a model adopted from the literature on competitive location analysis. A mathematical programming formulation is presented. For small instances, the Pareto front can be determined exactly using an epsilon constraint method. For solving also realistic instances, an evolutionary algorithm has been implemented and evaluated. The algorithms are tested on real-world instances from Mozambique. The results show that when designing a distribution network, improvements can be achieved by taking the predicted behavior of beneficiaries into account.
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ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-015-2097-9